import torch
import torch.nn as nn
from .fusion_module import TransFusionModule
from .module import WTConv2d, DA_Block, SCA, TA, EFF, SEAttention, DFF, BFAM, CPAM, CPCABlock, EUCB

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

def gram_matrix(x):
    """计算输入特征图的Gram矩阵"""
    (b, c, h, w) = x.size()  # 获取输入x的形状
    features = x.view(b, c, h * w)  # 将特征图展平为2D张量
    gram = torch.bmm(features, features.transpose(1, 2))  # 计算Gram矩阵
    return gram / (c * h * w)  # 对矩阵进行归一化

class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=18, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None, leader=False, trans_fusion_info=None):
        super(ResNet, self).__init__()
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        self.gram_matrices = {}
        self.total_feature_maps = {}
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        self.leader = leader
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        # self.axial = AxialAttention(in_planes=64, out_planes=64, groups=1, kernel_size=224, stride=1, bias=False,
        #                             width=False)
        # self.block = DA_Block(64)
        # self.eff = EFF(512,is_bottom=False)
        # self.bfam = BFAM(self.inplanes,self.inplanes)
        # self.cpca = CPCABlock(self.inplanes,self.inplanes,channelAttention_reduce=4)
        self.cpam = CPAM(self.inplanes)
        # self.wt = WTConv2d(self.inplanes, self.inplanes)


        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        # self.eucb1 = EUCB(in_channels=64, out_channels=64)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        # self.eucb2 = EUCB(in_channels=128, out_channels=128)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.eucb3 = EUCB(in_channels=256, out_channels=256)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        # self.eucb4 = EUCB(in_channels=512, out_channels=512)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.features = ResNet

        self.fc = nn.Linear(512 * block.expansion, num_classes)

        if self.leader:
            if trans_fusion_info is not None:
                self.trans_fusion_module = TransFusionModule(trans_fusion_info[0], 7, model_num=trans_fusion_info[1])
            else:
                self.trans_fusion_module = TransFusionModule(512, 7)

        self.reset_parameters(zero_init_residual)
        self.register_hook()
        # self.se = SEAttention(channel=3)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes,
                            stride=stride, downsample=downsample, groups=self.groups,
                            base_width=self.base_width, dilation=previous_dilation, norm_layer=norm_layer))

        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes,
                                groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))
        return nn.Sequential(*layers)

    def register_hook(self):

        self.extract_layers = ['layer1', 'layer2', 'layer3', 'layer4']

        def get_activation(maps, name):
            def get_output_hook(module, input, output):
                maps[name+str(output.device)] = output

            return get_output_hook

        def add_hook(model, maps, extract_layers):
            for name, module in model.named_modules():
                if name in extract_layers:
                    module.register_forward_hook(get_activation(maps, name))

        add_hook(self, self.total_feature_maps, self.extract_layers)

        def add_cosine_similarity_gram_hook(model, gram_matrices, extract_layers):
            def cosine_similarity_gram_hook(module, input, output):
                B, C, H, W = output.size()
                F = output.view(B, C, H * W)

                # 计算每个通道的范数（L2范数）
                norm = torch.norm(F, dim=2, keepdim=True)

                # 计算余弦相似度
                F_normalized = F / (norm + 1e-8)  # 添加小值避免除零错误
                G = torch.bmm(F_normalized, F_normalized.transpose(1, 2))

                layer_name = module.__class__.__name__
                gram_matrices[layer_name] = G

            for name, module in model.named_modules():
                if name in extract_layers:
                    module.register_forward_hook(cosine_similarity_gram_hook)

        # 使用示例
        add_cosine_similarity_gram_hook(self, self.gram_matrices, self.extract_layers)

    def forward(self, x):
        # See note [TorchScript super()]
        # x = self.se(x)
        x = self.conv1(x)
        # x = self.block(x)
        # x = self.cpca(x)
        x = self.cpam(x)
        # x = self.wt(x)

        # x = self.eff(x)

        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        # x = self.eucb1(x)
        x = self.layer2(x)
        # x = self.eucb2(x)
        x = self.layer3(x)
        # x = self.eucb3(x)
        x = self.layer4(x)
        # x = self.eucb4(x)

        if self.leader:
            trans_fusion_output = self.trans_fusion_module(x)


        x = self.avgpool(x)

        x = torch.flatten(x, 1)
        x = self.fc(x)


        if self.leader:
            return x, trans_fusion_output
        else:
            return x

    def reset_parameters(self, zero_init_residual):

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

def ResNet18(**kwargs):

    return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)

def ResNet34(**kwargs):

    return ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
